Top Hybrid Machine Learning Engineer Jobs

Build and iterate machine learning models and proofs-of-concept using TrainingPeaks' athlete and coach data. Collaborate with product and engineering, debug and monitor models, interface with customers for feedback, and stay current on applied ML and AI techniques to deliver production-ready solutions.

The Senior Machine Learning Engineer will lead the development of scalable ML systems, mentor team members, and improve existing data infrastructures while collaborating with various stakeholders to execute strategic initiatives.

The Senior Machine Learning Engineer at Chewy will develop and deploy ML models for sponsored ads, collaborating across teams to optimize advertising strategies. Responsibilities include model ideation, experimentation, and mentoring junior scientists while communicating insights to leadership.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

This role involves developing AI and machine learning models for vaccine strain selection, integrating biological data, and validating models through rigorous benchmarking while collaborating with various scientific disciplines.

Design and implement AI capabilities, transition prototypes to production, build agent workflows, optimize data retrieval, and automate evaluation processes.

The Staff Machine Learning Engineer will lead ML systems design and deployment, mentor engineers, collaborate with teams, and communicate solutions effectively to stakeholders.

Lead the design and deployment of machine learning models to detect fraud, collaborating with teams to optimize performance and effectiveness while integrating solutions into existing systems.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

The role involves designing and implementing ML systems for pricing, managing data pipelines, and improving model performance and reliability. It requires collaboration with cross-functional teams and mentorship for engineers.